Diagnostic Accuracy of Corneal and Epithelial Thickness Map Parameters to Detect Keratoconus and Suspect Keratoconus

Abstract

Aim: To establish the diagnostic accuracy of corneal and epithelial thickness measurements obtained by spectral-domain optical coherence tomography (SD-OCT) in detecting keratoconus (KC) and suspect keratoconus (SKC).

Methods: This retrospective study reviewed the data of 144 eyes separated into three groups by the Sirius automated corneal classification software: normal (N) (n = 65), SKC (n = 43), and KC (n = 36). Corneal thickness (CT) and epithelial thickness (ET) in the central (0-2 mm) and paracentral (2-5 mm) zones were obtained with the Cirrus high-definition OCT. Areas under the curve (AUC) of receiver operator characteristic (ROC) curves were compared across groups to estimate their discrimination capacity.

Results: ROC curve analysis revealed excellent predictive ability for ET variables: minimum (Min) ET (0_2), minimum-maximum (Min-Max) ET (0_2), superonasal-inferotemporal (SN-IT) ET (2_5), Min-Max ET (2_5), and Min ET (2_5) to detect keratoconus (AUC > 0.9, all). Min-Max CT (0_2) was the only CT parameter with excellent ability to discriminate between KC and N eyes (AUC = 0.94; cutoff = ≤-32 μm). However, both ET and CT variables were not strong enough (AUC < 0.8, all) to differentiate between SKC and N eyes, with the highest diagnostic power for Min-Max ET (2_5) (AUC = 0.71; cutoff = ≤-9 μm) and central corneal thickness (CCT) (AUC = 0.76; cutoff = ≤533 μm).

Conclusion: These results demonstrate that OCT-derived CT and ET are able to differentiate between KC and N eyes, with a high level of certainty. However, Min-Max ET (2_5) was the parameter with the highest ability to detect suspect keratoconus.

Figure 1

High-definition AS-OCT (Zeiss Cirrus 5000) of a normal cornea.

Figure 2

The epithelial and topography maps of normal eye, suspect keratoconus, and keratoconus eyes. The green circles overlaid on epithelial thickness maps had diameters of 2.0 mm, 5.0 mm, 7 mm, and 9 mm. The top row was a randomly chosen normal left eye of a 26-year-old female. The topographic simulated K readings were 45.87 D and 47.23 D. The Sirius software classifier provided a normal class. Min-Max ET (2_5) and SN-IT ET (2_5) had normal values (−6 μm and −1 μm, respectively). Case 2 (middle row) was a 20-year-old female with suspect keratoconus in the right eye. Her CDVA was 1.0. The simulated K values were 43.83 D and 50.01 D. The topography map showed inferior steepening. The ET map showed apical thinning inferotemporally. Min-Max ET (2_5) and SN-IT ET (2_5) exceeded cutoff values (−14 μm and 8 μm, respectively). Case 3 (third row) was a 32-year-old female with keratoconus in her right eye. Her CDVA was 0.2. The simulated K readings were 46.61 D and 48.64 D. The Sirius software classifier showed keratoconus class. The ET map showed apical thinning with surrounding thickening. Min ET (0_2), Min-Max ET (0_2), Min ET (2_5), Min-Max ET (2_5), and SN-IT ET (2_5) exceeded cutoff values of detecting keratoconus (37 μm, −10 μm, 38 μm, −26 μm, and 9 μm, respectively). S = superior; T = temporal; I = inferior; N = nasal; K = keratometry; D = diopter; Min-Max = minimum-maximum; ET = epithelium thickness; SN-IT = superonasal-inferotemporal; μm = micron; CDVA = corrected distance visual acuity; Min = minimum.

Figure 3

Comparison of corneal thickness parameters that showed the best area under the receiver operating characteristic curves to differentiate between suspect keratoconus and normal eyes. CCT = central corneal thickness; Max = maximum; Min = minimum; Avg = average; AUC = area under the receiver operating characteristic curve; SE = standard error; CI = confidence interval.

Figure 4

Comparison of the corneal thickness parameters that showed the best area under the receiver operating characteristic curves to differentiate between keratoconus and normal eyes. Min-Max = minimum-maximum; CT = corneal thickness; Min = minimum; CCT = central corneal thickness; AUC = area under the receiver operating characteristic curve; SE = standard error; CI = confidence interval.

Figure 5

Comparison of the epithelial thickness parameters that showed the best area under the receiver operating characteristic curves to differentiate between suspect keratoconus and normal eyes. Min-Max = minimum-maximum; ET = epithelial thickness; SN-IT = superonasal-inferotemporal; Min = minimum; AUC = area under the receiver operating characteristic curve; SE = standard error; CI = confidence interval.

Figure 6

Comparison of the epithelial thickness parameters that showed the best area under the receiver operating characteristic curves to differentiate between keratoconus and normal eyes. SN-IT = superonasal-inferotemporal; ET = epithelial thickness; Min = minimum; Min-Max = minimum-maximum; AUC = area under the receiver operating characteristic curve; SE = standard error; CI = confidence interval.